apps/infsh/lightning-wan-2-2-i2v-a14b-offload

lightning-wan-2-2-i2v-a14b-offload

image to video with wan2.2 using the Lightning LoRA (non-fused) for fast generation

run in browser run via API

run with your agent

# install belt

$curl -fsSL https://cli.inference.sh | sh

# view schema & details

$belt app get infsh/lightning-wan-2-2-i2v-a14b-offload

# run

$belt app run infsh/lightning-wan-2-2-i2v-a14b-offload

api reference

about

image to video with wan2.2 using the lightning lora (non-fused) for fast generation

1. calling the api

install the client

the client provides a convenient way to interact with the api.

bash

1pip install inferencesh

setup your api key

set INFERENCE_API_KEY as an environment variable. get your key from settings → api keys.

bash

1export INFERENCE_API_KEY="inf_your_key"

run and get result

submit a request and wait for the final result. best for batch processing or when you don't need progress updates.

python

1from inferencesh import inference23client = inference()456result = client.run({7        "app": "infsh/lightning-wan-2-2-i2v-a14b-offload",8        "input": {}9    })1011print(result["output"])

stream live updates

get real-time progress updates as the task runs. ideal for showing progress bars, partial results, or long-running tasks.

python

1from inferencesh import inference23client = inference()456# stream=True yields updates as they arrive7for update in client.run({8        "app": "infsh/lightning-wan-2-2-i2v-a14b-offload",9        "input": {}10    }, stream=True):11    if update.get("progress"):12        print(f"progress: {update['progress']}%")13    if update.get("output"):14        print(f"output: {update['output']}")

2. authentication

the api uses api keys for authentication. see the authentication docs for detailed setup instructions.

3. files

file inputs are automatically handled by the sdk. you can pass local paths, urls, or base64 data.

automatic upload

the python sdk automatically detects local file paths and uploads them. urls are passed through as-is.

python

1# local file paths are automatically uploaded2result = client.run({3    "app": "infsh/lightning-wan-2-2-i2v-a14b-offload",4    "input": {5        "image": "/path/to/local/image.png",  # detected & uploaded6        "audio": "https://example.com/audio.mp3",  # url passed through7    }8})

manual upload

you can also upload files manually and use the returned url.

python

1# upload and get a hosted URL2file = client.files.upload("/path/to/file.png")3print(file.uri)  # https://cloud.inference.sh/...

4. webhooks

get notified when a task completes by providing a webhook url. when the task reaches a terminal state (completed, failed, or cancelled), a POST request is sent to your url with the task result.

python

1result = client.run({2    "app": "infsh/lightning-wan-2-2-i2v-a14b-offload",3    "input": {},4    "webhook": "https://your-server.com/webhook"5}, wait=False)

webhook payload

your endpoint receives a JSON POST with the task result:

json

1{2  "id": "task_abc123",3  "status": 9,4  "output": { ... },5  "error": "",6  "session_id": null,7  "created_at": "2024-01-15T10:30:00Z",8  "updated_at": "2024-01-15T10:30:05Z"9}

idstring— task id

statusnumber— terminal status (9=completed, 10=failed, 11=cancelled)

outputobject— task output (when completed)

errorstring— error message (when failed)

session_idstring— session id (if using sessions)

created_atstring— iso timestamp

updated_atstring— iso timestamp

5. schema

input

boundary_rationumber

boundary ratio between high and low noise transformers (0-1)

default: 0.875min:0max:1

end_imagestring(file)

optional end frame image; when provided, the video will transition from the first frame to this last frame

fpsinteger

frames per second for the output video

default: 16

imagestring(file)*

image

negative_promptstring

negative prompt to guide what to avoid in generation

default: "oversaturated, overexposed, static, blurry details, subtitles, stylized, artwork, painting, still image, overall gray, worst quality, low quality, JPEG artifacts, ugly, deformed, extra fingers, poorly drawn hands, poorly drawn face, malformed, disfigured, deformed limbs, fused fingers, static motionless frame, cluttered background, three legs, crowded background, walking backwards"

num_framesinteger

number of frames to generate

default: 81

num_inference_stepsinteger

number of denoising steps (4 or 6 are typically enough for lightning)

default: 4

promptstring*

text prompt for video generation

resolutionstring

resolution for the output video

default: "480p"

options:"480p""720p"

seedinteger

random seed for reproducibility

output

filestring(file)*

file

output_metaobject

structured metadata about inputs/outputs for pricing calculation

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